In life science, “big data” refers to large, complex datasets that cannot be effectively managed using traditional data-processing software. The substantial volume of life sciences data can lead to security concerns and data silos, among other issues.

This guide provides comprehensive insights into managing big data in life sciences. We’ll run through exactly what is meant by big data, discuss why it is important in the life science industry, and outline some of the strategies that can be used to manage big data.

What is big data?

Big data is a term that is used to describe large, complex, and hard-to-manage volumes of data that cannot be dealt with using traditional data-processing software. 

In the field of life science, a vast amount of big data is generated daily, stemming from a diverse range of sources like experiments, health records, and health screenings. This data has a pivotal role in making scientific advancements and breakthroughs.

To explain the concept in more detail, “big data” is often broken down into the three Vs: volume, velocity, and variety.

The three Vs of big data


Volume refers to the amount of data. When large volumes of data are generated every single day, it needs to be processed and analyzed with specialized infrastructure because traditional storage technology is unable to deal with this data.


Velocity refers to the rate at which data is generated, collected, and processed. In the life science industry, data is being continuously generated from a range of sources, creating a significant velocity of data. This continuous generation of data highlights the need for efficient data management to effectively handle this constant stream of data and allow for timely analysis.


Variety refers to the different types of formats of data that are generated, collected, and stored. Life sciences data can typically be categorized in one of the following formats:

  • Structured data: This is well-organized data that is often found in databases. This can include data generated from clinical trials or data from laboratory experiments.
  • Unstructured data: This is data that is not stored in a structured database. In the context of life science, this may include notes in a notebook following an experiment.
  • Semi-structured data: This is data that contains features of structured and unstructured data. Within life science, unstructured data may be formatted as XML.

Benefits of big data in life sciences

Big data plays a key role in the life science industry. Big data’s benefits to the life sciences include the ability to:

1) Identify trends early

Big data in life sciences makes it easier to identify trends earlier. By analyzing big data, scientists can highlight patterns and trends that can help predict disease outbreaks, track disease progression, and implement preventative measures, ultimately saving lives.

2) Provide tailored medicine

In modern medicine, many treatment plans are influenced by the findings of clinical research. Integrating a range of big data can help physicians better understand patients and create personalized treatment plans. Big data allows for the analysis of individual genetic profiles, clinical histories, and lifestyle data. This facilitates the development of personalized treatment plans tailored to each patient’s unique characteristics, improving healthcare outcomes.

3) Drive decisions and solutions

Big data analytics empowers healthcare providers, researchers, and policymakers to make data-driven decisions. This leads to more informed choices regarding patient care, research priorities, and public health policies.

Challenges posed by big data in life sciences

Despite its advantages, big data in the life sciences can be challenging to manage for the following reasons:

Data protection

Big data is transforming life science, but as increasingly personal information is gathered, the demand for more robust data protection methods has grown. Organizations that manage life sciences data must ensure that they secure data in compliance with relevant regulations and policies to protect the privacy of patients.


The volume of big data that is generated, analyzed, and stored in life science continues to grow significantly on a daily basis. This means that larger, more efficient storage and processing solutions are required. It also means that, because of the sheer amount of data and research available, researchers have a harder time absorbing and using this information. Consequently, many scientists miss out on opportunities to access information that could potentially inform their research endeavors.

Technological advances

Big data is still relatively new and its rapid growth within the life sciences industry has outpaced technological solutions. The sheer scale at which big data is produced within the industry has meant that there is a lack of tools and systems capable of effectively managing and keeping pace with this ever-expanding dataset.

Big data poses challenges for many organizations within the life sciences because they collect, generate, process, and analyze data from a range of different sources. Many of these organizations are ill-equipped to handle the sheer volume of data produced from these sources. To overcome this issue, life science organizations need to prioritize effective data governance and integrations. 

Strategies for managing big data in life sciences

Within the life sciences industry, the management of big data is being transformed through the adoption of technological solutions such as cloud computing, AI, and machine learning. 

Cloud computing

The sheer volume of big data in life science requires a scalable, flexible, and central platform. For this reason, cloud-based platforms are a favored solution among life science organizations looking to effectively manage big data.

Cloud-based platforms are both scalable and flexible, allowing organizations to increase or reduce their storage or computing requirements to better support their needs.

By migrating to a cloud-based platform, organizations can benefit from a system which consolidates information from a diverse range of sources. This streamlined approach to big data management prevents data silos and ensures researchers can easily access data, which can significantly accelerate data retrieval and analysis.

AI and machine learning 

AI and machine learning can significantly improve big data management by analyzing large amounts of data, identifying patterns, and making predictions about future trends.

Manage big data with Sapio Jarvis

Big data describes data that is large, complex, and hard to manage. However, despite this, it plays a crucial role in scientific discoveries and advancements. Within the life science industry, the best approach to managing data requires a solution that centralizes instrument and lab information system data, unifying it with scientific context and FAIR principals, providing built-in tools for scientists, and making data easily accessible to machine learning algorithms and artificial intelligence models.

Sapio Jarvis is an all-in-one scientific data cloud that combines LIMS system, ELN software, and sophisticated data integration tools in one unified platform. By harmonizing scientific intelligence across your lab, as well as connecting with your trusted scientists, Jarvis offers more context for truly unified insights.

To find out more about Sapio Jarvis, or any of our solutions, get in touch or request a demo today.